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How does AI affect urban carbon emissions? Quasi-experimental evidence from China's AI innovation and development pilot zones

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  • Zhang, Kun
  • Kou, Zi-Xuan
  • Zhu, Pei-Hua
  • Qian, Xiang-Yan
  • Yang, Yun-Ze

Abstract

Artificial intelligence (AI) is gradually becoming an important engine for empowering urban economies, driving the low-carbon growth of cities. To promote the deep integration of AI with economic and social expansion, China has proposed building national new-generation AI innovation and development pilot zones (AIPZ) in cities. However, the impact of AIPZ construction on low-carbon development remains unclear. Using panel data from 276 cities in China from 2015 to 2022, we employed a difference-in-differences method to assess the impact of the AIPZ policy on urban carbon emissions (UCE). First, as verified by multiple robustness tests, the AIPZ policy effectively reduced UCE, with an average reduction of 3.4 %. Second, the policy helped lower urban pollutant emissions and achieved a synergistic effect of carbon and pollution reduction. Third, according to the mechanism analysis, the AIPZ policy promoted carbon reduction by optimizing the government spending structure and improving consumers’ online lifestyles. Additionally, the economic effect analysis revealed that the AIPZ policy reduced UCE by optimizing industrial structure, enhancing energy efficiency, and promoting technological innovation. Therefore, this study verifies the positive role of AI in reducing UCE and provides policy support for leveraging AI to promote green and low-carbon urban development.

Suggested Citation

  • Zhang, Kun & Kou, Zi-Xuan & Zhu, Pei-Hua & Qian, Xiang-Yan & Yang, Yun-Ze, 2025. "How does AI affect urban carbon emissions? Quasi-experimental evidence from China's AI innovation and development pilot zones," Economic Analysis and Policy, Elsevier, vol. 85(C), pages 426-447.
    • Handle: RePEc:eee:ecanpo:v:85:y:2025:i:c:p:426-447
    DOI: 10.1016/j.eap.2024.12.013
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    as
    1. Jeyhun I. Mikayilov & Marzio Galeotti & Fakhri J. Hasanov, 2018. "The Impact of Economic Growth on CO2 Emissions in Azerbaijan," IEFE Working Papers 102, IEFE, Center for Research on Energy and Environmental Economics and Policy, Universita' Bocconi, Milano, Italy.
    2. Qiang Wang & Yuanfan Li & Rongrong Li, 2024. "Ecological footprints, carbon emissions, and energy transitions: the impact of artificial intelligence (AI)," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-18, December.
    3. Wu, Jianxin & Wu, Yanrui & Guo, Xiumei & Cheong, Tsun Se, 2016. "Convergence of carbon dioxide emissions in Chinese cities: A continuous dynamic distribution approach," Energy Policy, Elsevier, vol. 91(C), pages 207-219.
    4. Li, Yaya & Zhang, Yuru & Pan, An & Han, Minchun & Veglianti, Eleonora, 2022. "Carbon emission reduction effects of industrial robot applications: Heterogeneity characteristics and influencing mechanisms," Technology in Society, Elsevier, vol. 70(C).
    5. Zhang, Yiren & Ran, Congjing, 2023. "Effect of digital economy on air pollution in China? New evidence from the “National Big Data Comprehensive Pilot Area” policy," Economic Analysis and Policy, Elsevier, vol. 79(C), pages 986-1004.
    6. Nishant, Rohit & Kennedy, Mike & Corbett, Jacqueline, 2020. "Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda," International Journal of Information Management, Elsevier, vol. 53(C).
    7. Chai, Jian & Tian, Lingyue & Jia, Ruining, 2023. "New energy demonstration city, spatial spillover and carbon emission efficiency: Evidence from China's quasi-natural experiment," Energy Policy, Elsevier, vol. 173(C).
    8. Diana Ivanova & Konstantin Stadler & Kjartan Steen-Olsen & Richard Wood & Gibran Vita & Arnold Tukker & Edgar G. Hertwich, 2016. "Environmental Impact Assessment of Household Consumption," Journal of Industrial Ecology, Yale University, vol. 20(3), pages 526-536, June.
    9. Lyu, Wenjing & Liu, Jin, 2021. "Artificial Intelligence and emerging digital technologies in the energy sector," Applied Energy, Elsevier, vol. 303(C).
    10. Shu, Yunxia & Deng, Nanxin & Wu, Yuming & Bao, Shuming & Bie, Ao, 2023. "Urban governance and sustainable development: The effect of smart city on carbon emission in China," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    11. Wang, Linhui & Chen, Qi & Dong, Zhiqing & Cheng, Lu, 2024. "The role of industrial intelligence in peaking carbon emissions in China," Technological Forecasting and Social Change, Elsevier, vol. 199(C).
    12. Hong, Qianqian & Cui, Linhao & Hong, Penghui, 2022. "The impact of carbon emissions trading on energy efficiency: Evidence from quasi-experiment in China's carbon emissions trading pilot," Energy Economics, Elsevier, vol. 110(C).
    13. Yu, Yantuan & Zhang, Ning, 2021. "Low-carbon city pilot and carbon emission efficiency: Quasi-experimental evidence from China," Energy Economics, Elsevier, vol. 96(C).
    14. Li, Yaya & Zhang, Yun, 2023. "What is the role of green ICT innovation in lowering carbon emissions in China? A provincial-level analysis," Energy Economics, Elsevier, vol. 127(PA).
    15. Zhang, Kun & Zhu, Pei-Hua & Qian, Xiang-Yan, 2024. "National information consumption demonstration city construction and urban green development: A quasi-experiment from Chinese cities," Energy Economics, Elsevier, vol. 130(C).
    16. Zhichuan Zhu & Bo Liu & Zhuoxi Yu & Jianhong Cao, 2022. "Effects of the Digital Economy on Carbon Emissions: Evidence from China," IJERPH, MDPI, vol. 19(15), pages 1-21, August.
    17. Lee, Chien-Chiang & Yan, Jingyang, 2024. "Will artificial intelligence make energy cleaner? Evidence of nonlinearity," Applied Energy, Elsevier, vol. 363(C).
    18. Yaya Li & Yuru Zhang & An Pan & Minchun Han & Eleonora Veglianti, 2022. "Carbon emission reduction effects of industrial robot applications: Heterogeneity characteristics and influencing mechanisms," Post-Print hal-04522085, HAL.
    19. Liu, Jun & Chang, Huihong & Forrest, Jeffrey Yi-Lin & Yang, Baohua, 2020. "Influence of artificial intelligence on technological innovation: Evidence from the panel data of china's manufacturing sectors," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    20. Xue, Mingfu & Razzaq, Asif & Afshan, Sahar & Yang, Xiaodong, 2023. "Fiscal pressure and carbon intensity: A quasi-natural experiment based on education authority reform," Energy Economics, Elsevier, vol. 126(C).
    21. Xue, Yan & Tang, Chang & Wu, Haitao & Liu, Jianmin & Hao, Yu, 2022. "The emerging driving force of energy consumption in China: Does digital economy development matter?," Energy Policy, Elsevier, vol. 165(C).
    22. Li, Juan & Ma, Shaoqi & Qu, Yi & Wang, Jiamin, 2023. "The impact of artificial intelligence on firms’ energy and resource efficiency: Empirical evidence from China," Resources Policy, Elsevier, vol. 82(C).
    23. Yin, Zhaohui & Jiang, Xiaomeng & Lin, Songyue & Liu, Jin, 2022. "The impact of online education on carbon emissions in the context of the COVID-19 pandemic – Taking Chinese universities as examples," Applied Energy, Elsevier, vol. 314(C).
    24. Huyen T. K. Le & Andre L. Carrel & Harsh Shah, 2022. "Impacts of online shopping on travel demand: a systematic review," Transport Reviews, Taylor & Francis Journals, vol. 42(3), pages 273-295, May.
    25. Hu, Wei & Fan, Yuemin, 2020. "City size and energy conservation: Do large cities in China consume more energy?," Energy Economics, Elsevier, vol. 92(C).
    26. Lynn H. Kaack & Priya L. Donti & Emma Strubell & George Kamiya & Felix Creutzig & David Rolnick, 2022. "Aligning artificial intelligence with climate change mitigation," Nature Climate Change, Nature, vol. 12(6), pages 518-527, June.
    27. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, Enero-Abr.
    28. Zhou, Wei & Zhuang, Yan & Chen, Yan, 2024. "How does artificial intelligence affect pollutant emissions by improving energy efficiency and developing green technology," Energy Economics, Elsevier, vol. 131(C).
    29. Yi, Ming & Liu, Yafen & Sheng, Mingyue Selena & Wen, Le, 2022. "Effects of digital economy on carbon emission reduction: New evidence from China," Energy Policy, Elsevier, vol. 171(C).
    30. Ding, Tao & Li, Jiangyuan & Shi, Xing & Li, Xuhui & Chen, Ya, 2023. "Is artificial intelligence associated with carbon emissions reduction? Case of China," Resources Policy, Elsevier, vol. 85(PB).
    31. Chen, Yang & Cheng, Liang & Lee, Chien-Chiang, 2022. "How does the use of industrial robots affect the ecological footprint? International evidence," Ecological Economics, Elsevier, vol. 198(C).
    32. Lyu, Yanwei & Xiao, Xuan & Zhang, Jinning, 2024. "Does the digital economy enhance green total factor productivity in China? The evidence from a national big data comprehensive pilot zone," Structural Change and Economic Dynamics, Elsevier, vol. 69(C), pages 183-196.
    33. Xuan Chang & Jinye Li, 2022. "Effects of the Digital Economy on Carbon Emissions in China: A Spatial Durbin Econometric Analysis," Sustainability, MDPI, vol. 14(24), pages 1-20, December.
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    1. Huang, Meiying & Li, Quan & Li, Bowen, 2025. "From algorithms to green growth: Can artificial intelligence drive enterprise energy transformation?," Economic Analysis and Policy, Elsevier, vol. 85(C), pages 1846-1866.

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